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273 lines
8.5 KiB
Python
273 lines
8.5 KiB
Python
import re
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import click
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import pandas as pd
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from io import StringIO
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import os
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import xml.etree.ElementTree as ET
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import requests
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import unicodedata
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import json
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@click.command()
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@click.argument('tsv-file', type=click.Path(exists=True), required=True, nargs=1)
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@click.argument('url-file', type=click.Path(exists=False), required=True, nargs=1)
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def extract_document_links(tsv_file, url_file):
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parts = extract_doc_links(tsv_file)
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urls = [part['url'] for part in parts]
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urls = pd.DataFrame(urls, columns=['url'])
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urls.to_csv(url_file, sep="\t", quoting=3, index=False)
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@click.command()
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@click.argument('tsv-file', type=click.Path(exists=True), required=True, nargs=1)
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@click.argument('annotated-tsv-file', type=click.Path(exists=False), required=True, nargs=1)
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def annotate_tsv(tsv_file, annotated_tsv_file):
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parts = extract_doc_links(tsv_file)
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annotated_parts = []
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for part in parts:
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part_data = StringIO(part['header'] + part['text'])
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df = pd.read_csv(part_data, sep="\t", comment='#', quoting=3)
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df['url_id'] = len(annotated_parts)
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annotated_parts.append(df)
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df = pd.concat(annotated_parts)
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df.to_csv(annotated_tsv_file, sep="\t", quoting=3, index=False)
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def extract_doc_links(tsv_file):
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parts = []
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header = None
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with open(tsv_file, 'r') as f:
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text = []
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url = None
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for line in f:
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if header is None:
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header = "\t".join(line.split()) + '\n'
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continue
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urls = [url for url in
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re.findall(r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', line)]
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if len(urls) > 0:
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if url is not None:
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parts.append({"url": url, 'header': header, 'text': "".join(text)})
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text = []
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url = urls[-1]
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else:
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if url is None:
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continue
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line = '\t'.join(line.split())
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if line.count('\t') == 2:
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line = "\t" + line
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if line.count('\t') >= 3:
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text.append(line + '\n')
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continue
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if line.startswith('#'):
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continue
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if len(line) == 0:
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continue
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print('Line error: |', line, '|Number of Tabs: ', line.count('\t'))
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if url is not None:
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parts.append({"url": url, 'header': header, 'text': "".join(text)})
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return parts
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def ner(tsv, ner_rest_endpoint):
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resp = requests.post(url=ner_rest_endpoint, json={'text': " ".join(tsv.TOKEN.tolist())})
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def iterate_ner_results(result_sentences):
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for sen in result_sentences:
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for token in sen:
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yield unicodedata.normalize('NFC', token['word']), token['prediction'], False
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yield '', '', True
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ner_result = json.loads(resp.content)
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result_sequence = iterate_ner_results(ner_result)
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tsv_result = []
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for idx, row in tsv.iterrows():
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row_token = unicodedata.normalize('NFC', row.TOKEN.replace(' ', ''))
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ner_token_concat = ''
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while row_token != ner_token_concat:
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ner_token, ner_tag, sentence_break = next(result_sequence)
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ner_token_concat += ner_token
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assert len(row_token) >= len(ner_token_concat)
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if sentence_break:
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tsv_result.append((0, '', 'O', 'O', '-', row.url_id, row.left, row.right, row.top, row.bottom))
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else:
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tsv_result.append((0, ner_token, ner_tag, 'O', '-', row.url_id, row.left, row.right, row.top,
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row.bottom))
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return pd.DataFrame(tsv_result, columns=['No.', 'TOKEN', 'NE-TAG', 'NE-EMB', 'ID', 'url_id',
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'left', 'right', 'top', 'bottom']), ner_result
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def ned(tsv, ner_result, ned_rest_endpoint):
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resp = requests.post(url=ned_rest_endpoint + '/parse', json=ner_result)
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ner_parsed = json.loads(resp.content)
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resp = requests.post(url=ned_rest_endpoint + '/ned', json=ner_parsed, timeout=3600000)
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ned_result = json.loads(resp.content)
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rids = []
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entity = ""
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entity_type = None
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for rid, row in tsv.iterrows():
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if (entity != "") and ((row['NE-TAG'] == 'O') or (row['NE-TAG'].startswith('B-'))):
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eid = entity + "-" + entity_type
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if eid in ned_result:
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candidates = ned_result[eid]
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tsv.loc[rids, 'ID'] = candidates[0][1]['wikidata']
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rids = []
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entity = ""
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entity_type = None
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if row['NE-TAG'] != 'O':
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entity_type = row['NE-TAG'][2:]
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entity += " " if entity != "" else ""
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entity += row['TOKEN']
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rids.append(rid)
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return tsv
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@click.command()
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@click.argument('page-xml-file', type=click.Path(exists=True), required=True, nargs=1)
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@click.argument('tsv-out-file', type=click.Path(), required=True, nargs=1)
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@click.option('--image-url', type=str, default='http://empty')
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@click.option('--ner-rest-endpoint', type=str, default=None,
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help="REST endpoint of sbb_ner service. See https://github.com/qurator-spk/sbb_ner for details.")
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@click.option('--ned-rest-endpoint', type=str, default=None,
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help="REST endpoint of sbb_ned service. See https://github.com/qurator-spk/sbb_ned for details.")
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@click.option('--noproxy', type=bool, is_flag=True, help='disable proxy. default: enabled.')
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@click.option('--scale-factor', type=float, default=0.5685, help='default: 0.5685')
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def page2tsv(page_xml_file, tsv_out_file, image_url, ner_rest_endpoint, ned_rest_endpoint, noproxy, scale_factor):
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out_columns = ['No.', 'TOKEN', 'NE-TAG', 'NE-EMB', 'ID', 'url_id', 'left', 'right', 'top', 'bottom']
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if noproxy:
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os.environ['no_proxy'] = '*'
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tree = ET.parse(page_xml_file)
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xmlns = tree.getroot().tag.split('}')[0].strip('{')
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urls = []
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if os.path.exists(tsv_out_file):
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parts = extract_doc_links(tsv_out_file)
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urls = [part['url'] for part in parts]
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else:
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pd.DataFrame([], columns=out_columns). to_csv(tsv_out_file, sep="\t", quoting=3, index=False)
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tsv = []
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line_number = 0
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rgn_number = 0
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for region in tree.findall('.//{%s}TextRegion' % xmlns):
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rgn_number += 1
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for text_line in region.findall('.//{%s}TextLine' % xmlns):
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line_number += 1
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for words in text_line.findall('./{%s}Word' % xmlns):
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for word in words.findall('./{%s}TextEquiv/{%s}Unicode' % (xmlns, xmlns)):
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text = word.text
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for coords in words.findall('./{%s}Coords' % xmlns):
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# transform OCR coordinates using `scale_factor` to derive
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# correct coordinates for the web presentation image
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points = [int(scale_factor * float(pos))
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for p in coords.attrib['points'].split(' ') for pos in p.split(',')]
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x_points = [points[i] for i in range(0, len(points), 2)]
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y_points = [points[i] for i in range(1, len(points), 2)]
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left = min(x_points)
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right = max(x_points)
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top = min(y_points)
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bottom = max(y_points)
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tsv.append((rgn_number, line_number, left + (right-left)/2.0,
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0, text, 'O', 'O', '-', len(urls), left, right, top, bottom))
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with open(tsv_out_file, 'a') as f:
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f.write('# ' + image_url + '\n')
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tsv = pd.DataFrame(tsv, columns=['rid', 'line', 'hcenter'] + out_columns)
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vlinecenter = pd.DataFrame(tsv[['line', 'top']].groupby('line', sort=False).mean().top +
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(tsv[['line', 'bottom']].groupby('line', sort=False).mean().bottom -
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tsv[['line', 'top']].groupby('line', sort=False).mean().top) / 2, columns=['vlinecenter'])
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tsv = tsv.merge(vlinecenter, left_on='line', right_index=True)
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regions = [region.sort_values(['vlinecenter', 'hcenter']) for rid, region in tsv.groupby('rid', sort=False)]
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tsv = pd.concat(regions)
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tsv = tsv[out_columns].reset_index(drop=True)
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if ner_rest_endpoint is not None:
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tsv, ner_result = ner(tsv, ner_rest_endpoint)
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if ned_rest_endpoint is not None:
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tsv = ned(tsv, ner_result, ned_rest_endpoint)
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tsv.to_csv(tsv_out_file, sep="\t", quoting=3, index=False, mode='a', header=False)
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